Paper Title
Vehicle Detection Using Centroid Tracker Algorithm

As it is necessary for many applications, including traffic monitoring, autonomous driving, and surveillance systems, vehicle detection is a crucial task in the field of computer vision and machine learning. The Centroid Tracker Algorithm has been a well-liked and successful method for real-time vehicle detection and tracking in recent years. This approach locates and tracks automobiles based on their centroids by combining object identification and tracking methods. By identifying changes in a vehicle's position over time, the system can identify and track it. This abstract intends to give an overview of the machine learning-based Centroid Tracker Algorithm-based vehicle identification system by presenting its architecture, essential elements, and performance metrics. The system can accurately and efficiently detect and track multiple vehicles simultaneously, making it a useful tool in various applications.